Citations

ABSTRACT Real time, chronic electrochemical detection of neurotransmitters will provide a positive step in the treatment and understanding of neurological disease. However, current electrodes using carbon fibers (CF) fail to perform chronically. While diamond‐based coatings show promise in improving their longevity, achieving a uniform layer of such coatings on CFs is challenging, and the electrodes often lose sensitivity after coating. In this work, a complete and uniform boron‐doped nanocrystalline material grown in a diamond reactor (B‐NCD) was developed to coat CF microelectrodes for neurochemical sensing. The coating was characterized electrically, optically, mechanically, and chemically. The B‐NCD coated CF electrodes were able to detect phasic dopamine at a sensitivity comparable to the most widely used alternatives (uncoated and PEDOT:Nafion coated CFs). During biofouling testing, the B‐NCD coated CF electrodes demonstrated better stability than uncoated CFs and comparable performance to PEDOT:Nafion coated CFs. Moreover, B‐NCD exhibited no signs of degradation during consecutive FSCV applications, while uncoated and PEDOT:Nafion coated CF electrodes degraded significantly over time. Furthermore, the B‐NCD coating supported the survival and development of neurons and astrocytes in vitro, exhibited excellent adhesion and durability during mechanical bending testing, and enabled successful in vivo recording of phasic dopamine release in the rat brain. Overall, B‐NCD coated CFs present as an ideal candidate for chronic, flexible neural implantable electrodes for long‐term neurochemical monitoring.

OBJECTIVE Achieving submillimetric accuracy in stereotactic neurosurgery remains critical for safely targeting deep brain structures. Current workflows rely on intraoperative CT to register stereotactic frames to preoperative imaging, but this introduces additional radiation exposure, cost, and workflow complexity. Three-dimensional surface scanning (3DSS) potentially offers a fast radiation-free alternative capable of capturing detailed craniofacial anatomy and frame geometry. Therefore, the aim of this cadaveric study was to evaluate the feasibility, accuracy, and navigational compatibility of integrating 3DSS into stereotactic workflows as a potential replacement for intraoperative CT. METHODS A human cadaveric head was imaged with both thin-slice CT and structured-light 3DSS after mounting stereotactic frame components. CT, 3DSS, and computer-aided design (CAD) models were registered using a multistep point cloud alignment pipeline involving the fast point feature histogram, the random sample consensus method, and the iterative closest point algorithm. Accuracy was assessed using surface deviation error, root mean square error (RMSE), and fiducial registration error within a commercial neuronavigation platform. RESULTS Fusion of the 3D facial scan with CT provided a mean surface deviation error of approximately 0.4 mm. Registration of the 3DSS-derived N-bar localizer to its CAD reference model produced a frame alignment RMSE of 1.3 mm. Within the navigation system, the 3DSS-based workflow achieved a mean fiducial registration error of 0.14 mm (range 0.02–0.20 mm), outperforming the conventional CT-based method (mean error 0.20 mm, range 0.09–0.40 mm). CONCLUSIONS The 3DSS-based method enabled precise submillimetric stereotactic registration without the need for intraoperative CT, reducing radiation exposure and operative complexity. This workflow is fully compatible with existing navigation systems and could serve as a practical radiation-free alternative in stereotactic neurosurgery. Future work will focus on automating frame detection, incorporating artificial intelligence–driven fusion methods, and validating this approach in live surgical settings.

ABSTRACT Background and hypothesis End-stage renal disease patients require peritoneal dialysis (PD) as a life-saving therapy; however, they continue to experience systemic inflammation and increased cardiovascular pathology. These complications can be exacerbated by exposure to PD solutions (PDS) containing glucose. This highlights the clinical unmet need for novel osmotic agents to replace glucose. Here, we tested troxerutin, a Food and Drug Administration–approved drug, as a potential osmotic agent. Methods To evaluate troxerutin osmotic and non-toxic effects, in vitro and in vivo experiments were carried out using artificial membranes, mice and cultured human mesothelial cells (MCs). The local and systemic consequences of chronic peritoneal exposure to PDS associated to renal insufficiency were investigated in a preclinical model. 5/6 nephrectomized C57BL/6J mice were daily exposed to glucose- or troxerutin-PDS for 60 days, then evaluating tissue damage, proinflammatory markers and cardiac damage. Results In vitro and in vivo studies revealed that troxerutin exhibits similar osmotic and ultrafiltration capacities to glucose, but with colloidal properties. Moreover, only glucose, but not troxerutin, reduced MC viability, induced mesothelial-to-mesenchymal transition, and increased damage markers (such as interleukin-6 and vascular endothelial growth factor A). In the preclinical model, long-term treatment with glucose-PDS, but not troxerutin-PDS, significantly increased peritoneal membrane thickening, tissue proinflammatory markers (evaluated in peritoneum, kidney, heart and aorta) and cardiovascular damage (cardiac hypertrophy and fibrosis) in mice. These preclinical data clearly demonstrate the wide deleterious effects of chronic peritoneal exposure to glucose-containing PDS in mice with kidney insufficiency and that the replacement of glucose with troxerutin notably reduces these effects. Conclusion Troxerutin emerges as a more biocompatible alternative to glucose as an osmotic agent for PD therapy, exhibiting no adverse local or systemic side effects in preclinical studies. This research presents a novel approach to reduce cardiovascular complications of PD in end-stage kidney disease.

Background: Critical congenital heart disease (CCHD) screening algorithms based on pulse oximetry have up to 27% false positive rate at high altitudes (>2500 m) despite altitude-specific cutoff changes. We examined the added value of an adult-based AI model applied to phonocardiography (AI-PCG) in the digital stethoscope EKO Core 500 in these settings. Research Question: What is the diagnostic performance of AI-PCG model compared to pulse oximetry in detecting neonatal CCHD across different altitudes in Latin America? Methods: This observational, prospective, case-control study included newborns born at different altitudes from 0 to 4380 meters in Peru, Mexico, Colombia and Bolivia. All underwent preductal and postductal oximetry at least 18 hours after birth, electrocardiography and PCG with the digital stethoscope. All CCHD cases were confirmed by echocardiography. Non-CCHD cases were defined by echocardiography or clinically if they were alive for 1 month and had 1) negative pulse oximetry, 2) no hospitalization due to cardiac or pulmonary causes and 3) no cyanosis or pneumonia. Those with abnormal pulse oximetry, genetic syndromes or murmur detection underwent echocardiography. Diagnostic performance metrics [area under the receiving operating characteristic curve (AUROC), sensitivity, specificity and false positive rate] were evaluated per modality. Results: A total of 1152 newborns were enrolled, 725 at <2500 m [13 (1.8%) with CCHD] and 427 at >2500 m [6 (1.4%) with CCHD]. Out of 19 CCHD cases, 17 had positive pulse oximetry and 9 had a positive AI-PCG model. One newborn with severe coarctation of the aorta and atrial septal defect had negative pulse oximetry and AI-PCG model. Pulse oximetry showed superior AUROC (0.932 vs 0.664), sensitivity (89.5% vs 47.4%), specificity (97% vs 85.5%) and lower false positive rate (3% vs 14.5%) compared to the AI-PCG model across altitudes. Diagnostic accuracy varied with altitude (Figure): at <2500 m, pulse oximetry showed higher AUROC (0.999 vs 0.612) and lower false positive rate (0.1% vs 16%) than AI-PCG model, but at >2500 m, pulse oximetry had similar AUROC (0.794 vs 0.774) and lower false positive rate (7.8% vs 11.9%) than the AI-PCG model. Conclusion: Pulse oximetry has superior diagnostic performance than an adult-based AI-PCG model when screening for neonatal CCHD at <2500 m, but similar performance is found >2500 m.

ABSTRACT Suicidality contributes substantially to global morbidity and mortality, yet despite its heritability, its biological etiology remains largely elusive. We conducted multi-ancestry genome-wide association study meta-analyses of suicidal ideation (259,747 cases), suicide attempt (64,993 cases), suicide death (9,197 cases), and suicidal behavior (suicide attempt/ death, 75,300 cases), across 54 cohorts (e.g., the Psychiatric Genomics Consortium, Million Veteran Program, UK Biobank). We identified 77 significant loci across meta-analyses, including 59 previously unreported for suicidality. SNP-based heritability ranged from 2.0-6.7% and there were strong, yet incomplete, genetic correlations between suicidality phenotypes (0.70-0.88). Fine-mapping prioritized putative causal SNPs and 20 credible genes. Enrichment analyses implicated synaptic pathways and neuronal populations predominantly in subcortical brain regions (e.g., amygdala excitatory, medium spiny, hippocampal CA1-3). Together, these findings establish suicidality as a polygenic set of traits with both shared and distinct genetic influences, providing a foundation for future studies of suicide biology and etiology.

Objectives: Electronic health records (EHR)-based triggers (eTriggers) have been used to study diagnostic errors in the emergency department (ED), often with suboptimal performance. Our objective was to investigate incremental value of multi-factor machine learning (ML) approaches to improve eTrigger performance. Methods: Patients presenting to an academic ED were categorized into trigger-positive and trigger-negative using standard trigger (T) definitions: (T1) ED return visits resulting in admission within 10 days; (T2) care escalation from the inpatient unit to the ICU within 24 hours; and (T3) deaths within 24 hours of admission. We trained and evaluated 6 supervised ML models. Results: A total of 124,053 consecutive encounters (5791 T-positive and 118,262 T-negative) were included. Among the T-positive, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. The T-based positive predictive values (PPV) were 5.2% for T1, 8.2% for T2, and 6.5% for T3. ML models trained and evaluated on balanced training dataset and imbalanced test set had low classification performances (accuracy: 0.72–0.95; PPV: 0.00–0.16; F1-score: 0.00–0.23). Higher performances were observed in balanced test sets (accuracy: 0.80–0.97; PPV: 0.82–1.00; F1-score: 0.79–0.97). Comparing models trained on clinically annotated data with models trained on T-based labels identified other important factors. Conclusions: Utilizing machine learning to refine e-triggers slightly improves the identification of diagnostic errors, as evidenced by an increase in PPV values. We identified new potential factors contributing to ED diagnostic errors. These findings open new avenues to construct or modify more accurate e-triggers for diagnostic error identification.

ImportanceStudies suggest that everolimus may reduce the risk of rejection, cardiac allograft vasculopathy (CAV), chronic kidney disease (CKD), and cytomegalovirus (CMV) after heart transplant. Everolimus use is controversial because of data demonstrating higher infection deaths when everolimus is introduced de novo after transplant. It is unclear whether everolimus is safe and effective when initiated at 6 months posttransplant in children, a population in which median graft survival is limited to 15 years and randomized clinical trials are lacking.ObjectiveTo evaluate the safety and efficacy of everolimus combined with low-dose tacrolimus to prevent major adverse transplant events (MATEs) in children after heart transplant.Design, Setting, and ParticipantsMulticenter, randomized, open-label, clinical trial enrolling 211 patients who were alive 6 months after pediatric heart transplant at 25 US sites from February 2018 to August 2020. The last date of follow-up was April 17, 2023.InterventionsParticipants were randomized to receive everolimus and low-dose tacrolimus (n = 107) or standard-dose tacrolimus and mycophenolate mofetil (n = 104) for 30 months.Main Outcomes and MeasuresThe primary efficacy end point was the MATE-3 score at 30 months, a validated composite ordinal end point including acute cellular rejection, CAV, and CKD. The primary safety end point was the MATE-6 score, encompassing the MATE-3 score plus antibody-mediated rejection, infection, and posttransplant lymphoproliferative disorder.ResultsAmong 211 children randomized, the mean age was 8.2 (SD, 6.3) years, 97 (46%) underwent transplant for congenital heart disease, and 49 (23%) were treated for rejection before 6 months. At 30 months, the mean MATE-3 score did not differ between the 2 treatment groups (mean difference, −0.32; 95% CI, −0.90 to 0.20; P = .16). The mean MATE-6 score was no higher in the everolimus group than in the mycophenolate group (baseline-adjusted mean difference, −0.40; 95% CI, −1.81 to 0.93), meeting the success criterion for safety (noninferiority margin &amp;lt;3). There were no differences in graft survival, MATE-free survival, or freedom from any individual MATE. Everolimus was associated with greater improvement in estimated glomerular filtration rate at 12 months (mean difference, 10.5 mL/min/1.73 m2; 95% CI, 1.09-19.91 mL/min/1.73 m2) and a lower incidence of CMV infection (hazard ratio, 0.50; 95% CI, 0.26-0.93).Conclusions and RelevanceAmong 6-month pediatric heart transplant survivors, everolimus and low-dose tacrolimus did not differ from tacrolimus and mycophenolate in preventing the composite of cellular rejection, CAV, and CKD at 30 months. However, everolimus and low-dose tacrolimus appear to be safe based on the total burden of 6 MATEs and may be associated with improved kidney function and less CMV infection.Trial RegistrationClinicalTrials.gov Identifier: NCT03386539

Importance Advances in Alzheimer disease (AD) have shifted research focus to earlier disease stages, necessitating more scalable approaches to identify cognitively unimpaired individuals with amyloid β (Aβ) pathology. Objective To assess the utility of plasma phosphorylated tau 217 (p-tau217) for classifying Aβ status in cognitively unimpaired individuals, both as a stand-alone test and in a 2-step approach where positive plasma results were confirmed using a second modality (Aβ positron emission tomography [PET] or cerebrospinal fluid [CSF]). Design, Setting, and Participants This cross-sectional cohort study used data collected between June 2009 and March 2024. We included 2916 cognitively unimpaired participants from 12 international independent observational cohorts in the US, Europe, Australia, and Canada with available plasma p-tau217 levels and CSF or PET Aβ biomarkers. Performance comparisons between mass spectrometry and immunoassay-based p-tau217 measurements were also performed (n = 964). Exposures Plasma p-tau217 levels measured by immunoassay. Main Outcome and Measures Aβ status, determined by CSF or Aβ PET biomarkers. Results Participants had a mean (SD) age of 66.9 (9.9) years; 971 (33.3%) were Aβ positive by either CSF or PET, 1667 (57.2%) were women, and 1108 (38.1%) carried at least 1 APOE ε4 allele. As a stand-alone test, plasma p-tau217 achieved a positive predictive value (PPV) of 79% (95% CI, 74-84) and an overall accuracy of 81% (95% CI, 80-82). In a 2-step workflow, the PPV and accuracy significantly increased to 91% (95% CI, 86-95). While this approach required screening of 677 individuals with plasma p-tau217 to identify 100 Aβ-positive individuals, compared to 536 participants when using PET alone, it reduced the need for PET testing to 124. Immunoassays demonstrated comparable PPVs to mass spectrometry (80% [95% CI, 74-86] vs 85% [95% CI, 81-90]; P = .12) but significantly lower overall accuracy (82% [95% CI, 79-84]% vs 88 [95% CI, 86-90]; P &amp;lt; .001) and true Aβ-positive detection rate (49% [95% CI, 43-55] vs 69% [95% CI, 64-75]; P &amp;lt; .001). Conclusions and Relevance The findings highlight the potential of plasma p-tau217 as a stand-alone test—or when used in a sequential 2-step approach alongside PET or CSF testing—as a cost-effective, scalable, and minimally burdensome strategy for identifying preclinical AD. Tailored screening workflows that incorporate p-tau217 can improve efficiency in participant selection for preclinical AD trials and, in the future, help guide access to disease-modifying treatments.

Abstract We investigated the influence of 55 583 autophagy-related single-nucleotide polymorphisms (SNPs) on chronic lymphocytic leukemia (CLL) risk across 4 independent populations comprising 5472 CLL cases and 726 465 controls. We also examined their impact on overall survival (OS), time to first treatment (TTFT), autophagy flux, and immune responses. A meta-analysis of the 4 populations identified, to our knowledge, for the first time, significant associations between CDKN2A (rs3731204) and BCL2 (rs4940571, rs12457371, and rs1026825) SNPs and CLL risk, with CDKN2A showing the strongest association (P = 1.57 × 10−12). We also validated previously reported associations for FAS, BCL2, and BAK1 SNPs with CLL risk (P = 4.73 × 10−21 to 3.39 × 10−9). The CDKN2Ars3731204 and FASrs1926194 SNPs associated with increased CDKN2A and ACTA2 messenger RNA expression levels in the whole blood and/or lymphocytes (P = 5.1 × 10−7, P = 1.58 × 10−21, and P = 7.8 × 10−41), although no significant effect on autophagy flux was observed. However, associations were found between CDKN2A, BCL2, and FAS SNPs and various T-cell subsets, cytokine production, and circulating concentrations of interferon gamma, tumor necrosis factor–related apoptosis-inducing ligand, CD40, chemokine ligand 20, and interleukin-2 receptor subunit β proteins (P ≤ .005). No significant association was detected between autophagy variants and OS or TTFT, suggesting that these variants drive disease initiation rather than progression. In conclusion, this study identified 4 novel associations for CLL and provided insights into the biological pathways that influence CLL development.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety. Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors. Design Consecutive cohort, retrospective observational design. Setting Academic ED with 80 000 annual visits. Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020. Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error. Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk. Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.